Bayesian change point Inference in time series analysis of COVID-19 pandemic dynamics

Document Type : Original Article

Author

• Masoud Majidizadeh

Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

10.22067/smps.2025.91421.1040

Abstract

The present study aims to estimate multiple change points in the time series data of confirmed COVID-19 cases and deaths, as well as to assess trends within the identified multiple change points in various countries. The data were analyzed using Poisson time series models that incorporate exogenous variables and autoregressive components, and the estimation of change points was conducted using the reversible jump Markov chain Monte Carlo method. Using the proposed method, we analyze the trajectory of cumulative COVID-19 cases and deaths in these countries, uncovering significant patterns that may have important implications for the effectiveness of pandemic responses across different nations. Furthermore, utilizing a change point detection algorithm in conjunction with a flexible time series model, we apply a forecasting method for COVID-19 and demonstrate its effectiveness in predicting the number of deaths in Japan.

Keywords

• Multiple change points
• Poisson time series data
• Posterior inferences
• Reversible jump Markov chain Monte Carlo

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